Set Options

knitr::opts_chunk$set(
  warning = TRUE, # show warnings during codebook generation
  message = TRUE, # show messages during codebook generation
  error = TRUE, # do not interrupt codebook generation in case of errors,
                # usually better for debugging
  inclue = TRUE,
  echo = TRUE  # show R code
)
ggplot2::theme_set(ggplot2::theme_bw())

library(codebook)
library(rio)
library(labelled)
## 
## Attaching package: 'labelled'
## The following object is masked from 'package:codebook':
## 
##     to_factor
library(flextable)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(stringr)

Prep Data

lab_info <- dir(path = "..", full.names = TRUE, recursive = TRUE, 
    include.dirs = TRUE, pattern = "Lab_Info.csv") %>%
 import()

# Load raw SPV data 
# Build data frame of valid SP verification responses
all_files <- list.files(path = "../data",
                         pattern = ".csv", 
                         full.names = TRUE,
                         recursive = TRUE)
all_files <- all_files[grepl("PP|SP|jatos_results|pp|sp|Pp|Sp", all_files)]

all_list <- list()
for (i in 1:length(all_files)){
  all_list[[i]] <- import(all_files[i])
  all_list[[i]]$unique_id <- all_files[i]
  
  all_list[[i]] <- all_list[[i]] %>% 
    mutate(across(everything(), as.character))
}

all_data <- bind_rows(all_list) %>% 
  select(subject_nr, 
         LAB_SEED, 
         datetime, 
         logfile, 
         task_order,
         List, 
         PList,
         Match,
         Orientation, 
         Probe,
         Target,
         response_time, 
         correct,
         PPList, 
         Orientation1,
         Orientation2, 
         Identical,
         Picture1,
         Picture2, 
         opensesame_codename,
         opensesame_version,
         unique_id) %>% 
  filter(!is.na(opensesame_codename))

all_data$PSA_ID <- substr(all_data$unique_id, 9, 16)
all_data$PSA_ID <- gsub("\\/", "", all_data$PSA_ID)

all_data$subject_nr[na.omit(all_data$opensesame_codename != "osweb")] <- 
  gsub("../data/|SP|PP|_|-| |.csv", "", all_data$unique_id[na.omit(all_data$opensesame_codename != "osweb")])

SP_V <- all_data %>% select(
  PSA_ID, subject_nr, LAB_SEED, datetime, logfile, task_order, List,
  Match, Orientation, PList, Probe, Target, response_time, correct, 
  opensesame_codename, opensesame_version) %>% 
  left_join(lab_info %>% select(PSA_ID, Language), by = "PSA_ID") %>% 
  readr::type_convert() %>% 
  distinct() %>% 
  mutate(Language = ifelse(Language == "Magyar", "Hungarian", Language)) %>% ## Switch "Magyar" to "Hungarian"
 mutate(Language = ifelse(Language == "Simple Chinese", "Simplified Chinese", Language)) %>% ## Switch "Simple Chinese" to "Simplified Chinese"
 mutate(PSA_ID = str_replace(PSA_ID, "SRB_002B", "SRB_002")) %>% ## Combine two Serbian language groups based on the collectors' recommendation
 mutate(Source = if_else(opensesame_codename == "osweb","osweb","site"), 
  Subject = paste0(Source,"_",PSA_ID,"_",subject_nr, "_", datetime)) %>% ## Compose the unique participant id
 subset(Match != "F") ## Exclude fillers in SP_V
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   PSA_ID = col_character(),
##   subject_nr = col_character(),
##   LAB_SEED = col_character(),
##   datetime = col_character(),
##   logfile = col_character(),
##   task_order = col_character(),
##   List = col_character(),
##   Match = col_character(),
##   Orientation = col_character(),
##   PList = col_character(),
##   Probe = col_character(),
##   Target = col_character(),
##   response_time = col_double(),
##   correct = col_double(),
##   opensesame_codename = col_character(),
##   opensesame_version = col_character(),
##   Language = col_character()
## )
# Build data frame of valid PP responses
PP <- all_data %>% select(
  PSA_ID, LAB_SEED, datetime, logfile, subject_nr, PPList, 
  Orientation1, Orientation2, Identical, Picture1, Picture2, 
  response_time, correct, opensesame_codename, opensesame_version) %>% 
  left_join(lab_info %>% select(PSA_ID, Language), by = "PSA_ID") %>% 
  readr::type_convert() %>% 
  distinct() %>% 
  mutate(Language = ifelse(Language == "Magyar", "Hungarian", Language)) %>% ## Switch "Magyar" to "Hungarian"
  mutate(Language = ifelse(Language == "Simple Chinese", "Simplified Chinese", Language)) %>% ## Switch "Simple Chinese" to "Simplified Chinese"
  mutate(PSA_ID = str_replace(PSA_ID, "SRB_002B", "SRB_002")) %>% ## Combine two Serbian language groups based on the collectors' recommendation
  mutate(Source = if_else(opensesame_codename == "osweb","osweb","site"), 
  Subject = paste0(Source,"_",PSA_ID,"_",subject_nr, "_", datetime)) %>%  ## Compose the unique participant id
  filter(Identical != "F") # practice trials 
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   PSA_ID = col_character(),
##   LAB_SEED = col_character(),
##   datetime = col_character(),
##   logfile = col_character(),
##   subject_nr = col_character(),
##   PPList = col_character(),
##   Orientation1 = col_character(),
##   Orientation2 = col_character(),
##   Identical = col_character(),
##   Picture1 = col_character(),
##   Picture2 = col_character(),
##   response_time = col_double(),
##   correct = col_double(),
##   opensesame_codename = col_character(),
##   opensesame_version = col_character(),
##   Language = col_character()
## )
SP_V$Subject[SP_V$opensesame_codename != "osweb"] <-
  paste0(SP_V$Source[SP_V$opensesame_codename != "osweb"],
         "_",
         SP_V$PSA_ID[SP_V$opensesame_codename != "osweb"],
         "_",
         SP_V$subject_nr[SP_V$opensesame_codename != "osweb"])

PP$Subject[PP$opensesame_codename != "osweb"] <- paste0(PP$Source[PP$opensesame_codename != "osweb"],"_",PP$PSA_ID[PP$opensesame_codename != "osweb"],"_",PP$subject_nr[PP$opensesame_codename != "osweb"])
PP$Subject[PP$Subject == "site_MYS_003_MYS003/MYS00324"] <- rep(c("site_MYS_003_MYS003/MYS00324", "site_MYS_003_MYS003/MYS00324_1"), each = 24)

SP_V$Subject[SP_V$Subject == "site_USA_173_USA173/USA17328
"] <- c(rep("site_USA_173_USA173_28", 30), rep("site_USA_173_USA173/USA17328_1", 29))

# fix issues with duplicate trials 
SP_V <- SP_V %>% 
  filter(!duplicated(SP_V %>% select(Subject, Target)))
PP <- PP %>% 
  filter(!duplicated(PP %>% select(Subject, Picture1)))

# test subjects counts
SP_V_subjects <- SP_V %>% 
  group_by(Subject, PSA_ID) %>% 
  count() 

PP_subjects <- PP %>% 
  group_by(Subject, PSA_ID) %>% 
  count()

merge_subjects <- SP_V_subjects %>% 
  full_join(PP_subjects, by = "Subject") %>% 
  rename(SP_Data = n.x,
         PP_Data = n.y)

merge_subjects$PSA_ID.x[is.na(merge_subjects$PSA_ID.x)] <- merge_subjects$PSA_ID.y[is.na(merge_subjects$PSA_ID.x)]

merge_subjects$PSA_ID <- merge_subjects$PSA_ID.x

merge_subjects <- merge_subjects %>% select(-PSA_ID.x, -PSA_ID.y)
osweb_meta <- dir(path = ".."
                  ,full.names = TRUE, recursive = TRUE, 
                  include.dirs = TRUE, pattern = "jatos_meta.csv") %>%
  import() %>%
  unique() %>% 
  mutate(Batch = str_replace(Batch, "SRB_002B", "SRB_002")) %>% 
  mutate(gender = ifelse(gender==1,"FEMALE",ifelse(gender==2,"MALE","MISSING"))) %>%
  # mutate(birth_year_tr = as.numeric(birth_year)) %>%
  mutate(birth_year_tr = as.numeric(gsub(birth_year,pattern="NA|x",replacement = ""))) %>%
  mutate(year = ifelse(birth_year_tr > 21 & !is.na(birth_year_tr), 1900 + birth_year_tr, 2000 + birth_year_tr)) %>%
  mutate(age = ifelse(!is.na(year),2021-year,NA)) %>%
  group_by(Batch) %>%
  summarise(N = n(), 
            Female_N = sum(gender=="FEMALE",na.rm = TRUE), 
            Male_N = sum(gender=="MALE", na.rm = T), 
            Age = mean(age, na.rm=TRUE), 
            AgeSD = sd(age, na.rm=TRUE), 
            Proficiency = mean(lang_prof),
            missing_age = sum(is.na(age)))

site_meta <- import("includes/files/insite_meta.csv") %>% 
  mutate(gender = ifelse(gender=="female","FEMALE",ifelse(gender=="male","MALE","MISSING"))) %>%
  unique() %>% 
  group_by(PSA_ID) %>% 
  summarize(N = n(), Female_N = sum(gender=="FEMALE", na.rm = TRUE), 
            Male_N = sum(gender=="MALE", na.rm = T), 
            Age = mean(age, na.rm=TRUE),
            AgeSD = sd(age, na.rm=TRUE), 
            missing_age = sum(is.na(age))
            )

merge_subjects <- merge_subjects %>% 
  group_by(PSA_ID) %>% 
  summarize(SP_N_trials = sum(SP_Data, na.rm = T),
            PP_N_trials = sum(PP_Data, na.rm = T),
            SP_N = length(na.omit(SP_Data)),
            PP_N = length(na.omit(PP_Data))) %>% 
  full_join(site_meta, by = "PSA_ID") %>% 
  full_join(osweb_meta, by = c("PSA_ID" = "Batch"))

merge_subjects <- merge_subjects %>% 
  group_by(PSA_ID) %>% 
  mutate(Overall_N_Gender = sum(c(N.x, N.y), na.rm = T),
         Overall_N_Female = sum(c(Female_N.x, Female_N.y), na.rm = T),
         Overall_N_Male = sum(c(Male_N.x, Male_N.y), na.rm = T),
         Overall_Age = mean(c(Age.x, Age.y), na.rm = T), 
         Overall_SD = mean(c(AgeSD.x, AgeSD.y), na.rm = T),
         Overall_Missing_Age = sum(c(missing_age.x, missing_age.y), na.rm = T)) %>% 
  left_join(lab_info %>% select(PSA_ID, Language) %>% unique(), by = "PSA_ID")
flextable(merge_subjects %>% 
            select(1:5, 19:25)) %>% 
  set_header_labels(c("Lab ID Code", "SP Number Trials", "PP Number Trials", 
                      "SP Participants", "PP Participants", "Demographic Sample Size", 
                      "Female Sample Size", "Male Sample Size", "Average Age", 
                      "SD Age", "Missing Age", "Language")) %>% 
  set_caption(caption = "Sample Size and Meta Data Information")
Sample Size and Meta Data Information

PSA_ID

SP_N_trials

PP_N_trials

SP_N

PP_N

Overall_N_Gender

Overall_N_Female

Overall_N_Male

Overall_Age

Overall_SD

Overall_Missing_Age

Language

ARE_001

1,248

1,248

52

52

53

0

0

38.00000

52

Arabic

ARE_002

1,296

1,296

54

54

54

42

12

26.51020

18.5877675

5

Arabic

AUS_002

2,376

2,376

99

99

103

46

37

20.14141

3.3228034

4

English

AUS_091

3,840

3,840

160

160

160

127

25

26.03185

11.5466836

3

English

AUT_002

2,400

2,400

100

100

114

0

1

20.94175

2.5622648

11

German

AUT_005

2,592

2,592

108

108

108

80

22

22.17925

4.2601077

2

German

BRA_003

1,200

1,200

50

50

50

36

13

30.80000

8.7341694

0

Brazilian Portuguese

CAN_020

2,352

2,376

98

99

104

54

40

20.25510

3.6589425

6

English

CHN_005

1,200

1,200

50

50

57

0

0

18.66038

3.9171389

4

Simple Chinese

CHN_019

840

816

35

34

39

0

1

25.17143

5.4367254

4

Simple Chinese

COL_001

1,680

1,656

70

69

70

0

0

21.35714

3.3623478

0

Spanish

DEU_020

624

624

26

26

26

18

3

23.88462

3.3861710

0

German

ECU_001

1,440

1,440

60

60

76

0

0

22.09722

4.2992293

4

Spanish

GBR_005

1,272

1,272

53

53

76

57

13

19.95890

3.8960035

3

English

GBR_006

1,200

1,200

50

50

51

37

13

20.14000

2.4578903

1

English

GBR_014

1,200

1,200

50

50

58

46

11

18.73684

1.6204752

1

English

GBR_043

720

720

30

30

32

15

11

25.70000

9.3960740

2

English

GRC_002

2,376

2,376

99

99

109

0

0

33.85981

11.3040722

2

Greek

HUN_001

3,610

3,816

151

159

168

3

1

21.50299

2.8153500

1

Magyar

IND_003

1,896

1,896

79

79

86

57

27

21.66265

3.4580310

3

Hindi

ISR_001

3,576

3,571

149

149

181

0

0

24.25309

9.2898875

19

Hebrew

MYS_003

1,200

1,248

50

52

52

38

11

22.56250

3.8971143

4

English

MYS_004

2,400

2,400

100

100

109

65

30

20.73148

2.0028320

1

English

NGA_001

1,248

1,248

52

52

52

24

22

23.94231

11.2901370

0

English

NOR_002

504

504

21

21

21

12

8

30.09524

8.5784892

0

Norwegian

NOR_003

1,320

1,320

55

55

53

1

1

23.55102

6.2452022

4

Norwegian

NOR_004

1,752

1,752

73

73

80

0

0

22.00000

4.3841509

2

Norwegian

NZL_005

7,680

7,680

320

320

320

244

56

23.21311

5.4287089

15

English

POL_001

1,368

1,368

57

57

146

0

0

23.24615

7.9553614

16

Polish

POR_001

1,488

1,464

62

61

55

26

23

30.74074

9.0890462

1

Portuguese

PSA_001

1,248

1,272

52

53

71

50

12

18.89231

0.9539896

6

English

PSA_002

1,536

1,536

64

64

102

79

11

19.82488

2.4172552

5

English

SRB_002

3,120

3,120

130

130

130

108

21

21.37500

4.5002187

2

Serbian

SVK_001

2,419

2,400

101

100

103

1

0

21.58824

2.5147208

1

Slovak

SVK_002

1,462

1,199

61

50

222

0

0

21.96172

2.1390304

13

Slovak

THA_001

1,200

1,152

50

48

50

29

9

21.54000

3.8130282

0

Thai

TUR_007

2,184

2,184

91

91

93

0

0

20.92308

2.9334499

2

Turkish

TUR_007E

264

264

11

11

12

9

2

20.36364

1.9116865

1

English

TUR_023

1,896

1,896

79

79

80

36

14

21.57895

8.6379822

3

Turkish

TUR_025

2,376

2,352

99

98

101

0

0

21.63000

2.1911438

1

Turkish

TWN_001

1,440

1,440

60

60

70

45

14

20.72727

1.2095194

4

Traditional Chinese

TWN_002

2,160

2,160

90

90

116

24

32

21.04347

3.6608571

17

Traditional Chinese

TWN_002E

288

288

12

12

12

6

5

21.16667

1.1934163

0

English

USA_011

1,512

1,512

63

63

63

30

23

22.34426

11.5453963

2

English

USA_020

7,980

8,064

333

336

403

258

76

19.63290

2.1214349

63

English

USA_030

648

648

27

27

31

20

3

36.00000

0.9607689

3

English

USA_032

1,209

1,224

51

51

51

30

21

19.29412

1.5138576

0

English

USA_033

3,000

3,024

125

126

129

90

25

20.05952

1.3554741

10

English

USA_065

1,200

1,200

50

50

61

35

15

18.85714

1.6340532

5

English

USA_173

792

744

33

31

3

0

3

19.66667

0.5773503

0

English

1

0

0

1

0000

3

0

1

19.00000

2

040

1

0

0

1

1

2

0

0

100.00000

1

11

2

0

0

2

123

1

0

0

1

1234

1

0

0

1

149

1

0

0

20.00000

0

173

1

0

1

22.00000

0

19

1

0

0

1

23232

1

0

0

1

26844

1

0

0

22.00000

0

3726

6

0

0

33.00000

5

54

1

0

0

23.00000

0

75AB

1

0

0

1

87

1

0

0

29.00000

0

9317

2

0

0

2

94

1

0

0

23.00000

0

9951

1

0

1

22.00000

0

BRA_004

1

0

0

1

Portuguese

MAC_001

1

0

0

1

MYS_033

1

1

0

19.00000

0

POL_003

1

0

0

1

SKV_002

1

0

0

22.00000

0

USA_002

1

0

0

1

USA_003

3

2

1

19.33333

0.5773503

0

jnkjn

1

0

0

1

codebook_data <- bind_rows(SP_V, PP)

var_label(codebook_data) <- list(
  PSA_ID = "Lab identification code",
  subject_nr = "Original, lab assigned subject number",
  LAB_SEED = "Original, lab assigned seed number for randomization",
  datetime = "Date and time of the study", 
  logfile = "Original location of the saved log",
  task_order = "", 
  List = "List file for the presentation of the stimuli",
  Match = "Match or Mismatch of the sentence and picture for sentence picture trials. F indicates practical trials.",
  Orientation = "Direction of the stimuli picture presented on the screen", 
  PList = "List file for the practice stimuli presentation",
  Probe = "Sentence seen in the sentence picture matching task",
  Target = "Object seen in the sentence picture matching task", 
  response_time = "Response time to determine if objects or sentence/picture matched",
  correct = "Indicates if the participant answered the trial correctly",
  opensesame_codename = "Name of the version of open sesame",
  opensesame_version = "Version number of the open sesame used",
  Language = "Language the participant took the study in",
  Source = "Online (osweb) versus in person (all others) data source",
  Subject = "A unique participant identifier, as duplicates and other repeating trials were fixed", 
  PPList = "The stimulus presentation list for picture picture matching task",
  Orientation1 = "The orientation of the first picture in the picture picture matching task",
  Orientation2 = "The orientation of the second picture in the picture picture matching task",
  Identical = "If the two orientations of the pictures matched in the picture picture matching task",
  Picture1 = "Name of the first picture in the picture picture matching task",
  Picture2 = "Name of the secon picture in the picture picture matching task"
  )

metadata(codebook_data)$name <- "Object Orientation across languages"
metadata(codebook_data)$description <- "This dataset includes the raw trial data from the PSA002: Object Orientation Across Languages Study. 

Mental simulation theories of language comprehension propose that people automatically create mental representations of objects mentioned in sentences. Representation is often measured with the sentence-picture verification task, in which participants first read a sentence implying the shape/size/color/object orientation and, on the following screen, a picture of an object. Participants then verify if the pictured object either matched or mismatched the implied visual information mentioned in the sentence. Previous studies indicated the match advantages of shapes, but findings concerning object orientation were mixed across languages. This registered report describes our investigation of the match advantage of object orientation across 18 languages, which was undertaken by multiple laboratories and organized by the Psychological Science Accelerator. The preregistered analysis revealed that there is no compelling evidence for a global match advantage, although some evidence of a match advantage in one language was found. Additionally, the match advantage was not predicted by mental rotation scores which does not support current embodied cognition theories."
metadata(codebook_data)$identifier <- "https://osf.io/e428p/"
metadata(codebook_data)$creator <- "Erin M. Buchanan"
metadata(codebook_data)$citation <- "Chen et al. (2023). Investigating Object Orientation Effects Across 18 Languages. Registered Report."
metadata(codebook_data)$url <- "https://osf.io/e428p/"
metadata(codebook_data)$datePublished <- "2023-02-10"
metadata(codebook_data)$temporalCoverage <- "2019-2021" 
metadata(codebook_data)$spatialCoverage <- "Online and in Person" 

Create codebook

codebook(codebook_data)

Metadata

Description

Dataset name: Object Orientation across languages

This dataset includes the raw trial data from the PSA002: Object Orientation Across Languages Study.

Mental simulation theories of language comprehension propose that people automatically create mental representations of objects mentioned in sentences. Representation is often measured with the sentence-picture verification task, in which participants first read a sentence implying the shape/size/color/object orientation and, on the following screen, a picture of an object. Participants then verify if the pictured object either matched or mismatched the implied visual information mentioned in the sentence. Previous studies indicated the match advantages of shapes, but findings concerning object orientation were mixed across languages. This registered report describes our investigation of the match advantage of object orientation across 18 languages, which was undertaken by multiple laboratories and organized by the Psychological Science Accelerator. The preregistered analysis revealed that there is no compelling evidence for a global match advantage, although some evidence of a match advantage in one language was found. Additionally, the match advantage was not predicted by mental rotation scores which does not support current embodied cognition theories.

Metadata for search engines
  • Temporal Coverage: 2019-2021

  • Spatial Coverage: Online and in Person

  • Citation: Chen et al. (2023). Investigating Object Orientation Effects Across 18 Languages. Registered Report.

  • URL: https://osf.io/e428p/

  • Identifier: https://osf.io/e428p/

  • Date published: 2023-02-10

  • Creator:

name value
1 Erin M. Buchanan
x
PSA_ID
subject_nr
LAB_SEED
datetime
logfile
task_order
List
Match
Orientation
PList
Probe
Target
response_time
correct
opensesame_codename
opensesame_version
Language
Source
Subject
PPList
Orientation1
Orientation2
Identical
Picture1
Picture2

#Variables

PSA_ID

Lab identification code

Distribution

Distribution of values for PSA_ID

Distribution of values for PSA_ID

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
PSA_ID Lab identification code character 0 1 50 0 7 8 0

subject_nr

Original, lab assigned subject number

Distribution

Distribution of values for subject_nr

Distribution of values for subject_nr

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
subject_nr Original, lab assigned subject number character 0 1 2844 0 1 22 0

LAB_SEED

Original, lab assigned seed number for randomization

Distribution

Distribution of values for LAB_SEED

Distribution of values for LAB_SEED

67464 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
LAB_SEED Original, lab assigned seed number for randomization character 67464 0.6479098 52 0 2 5 0

datetime

Date and time of the study

Distribution

Distribution of values for datetime

Distribution of values for datetime

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
datetime Date and time of the study character 0 1 6410 0 13 71 0

logfile

Original location of the saved log

Distribution

Distribution of values for logfile

Distribution of values for logfile

67416 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
logfile Original location of the saved log character 67416 0.6481603 5142 0 8 105 0

task_order

Distribution

Distribution of values for task_order

Distribution of values for task_order

129498 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
task_order character 129498 0.3241584 3 0 2 13 0

List

List file for the presentation of the stimuli

Distribution

Distribution of values for List

Distribution of values for List

95778 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
List List file for the presentation of the stimuli character 95778 0.5001409 68 0 1 15 0

Match

Match or Mismatch of the sentence and picture for sentence picture trials. F indicates practical trials.

Distribution

Distribution of values for Match

Distribution of values for Match

95778 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
Match Match or Mismatch of the sentence and picture for sentence picture trials. F indicates practical trials. character 95778 0.5001409 2 0 1 1 0

Orientation

Direction of the stimuli picture presented on the screen

Distribution

Distribution of values for Orientation

Distribution of values for Orientation

95778 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
Orientation Direction of the stimuli picture presented on the screen character 95778 0.5001409 2 0 1 1 0

PList

List file for the practice stimuli presentation

Distribution

Distribution of values for PList

Distribution of values for PList

101514 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
PList List file for the practice stimuli presentation character 101514 0.4702051 34 0 1 14 0

Probe

Sentence seen in the sentence picture matching task

Distribution

Distribution of values for Probe

Distribution of values for Probe

95778 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
Probe Sentence seen in the sentence picture matching task character 95778 0.5001409 1096 0 11 132 0

Target

Object seen in the sentence picture matching task

Distribution

Distribution of values for Target

Distribution of values for Target

95778 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
Target Object seen in the sentence picture matching task character 95778 0.5001409 48 0 8 22 0

response_time

Response time to determine if objects or sentence/picture matched

Distribution

Distribution of values for response_time

Distribution of values for response_time

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
response_time Response time to determine if objects or sentence/picture matched numeric 0 1 0 609 472186 815.4908 2870.269 ▇▁▁▁▁

correct

Indicates if the participant answered the trial correctly

Distribution

Distribution of values for correct

Distribution of values for correct

1 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
correct Indicates if the participant answered the trial correctly numeric 1 0.9999948 0 1 1 0.9289334 0.2569367 ▁▁▁▁▇

opensesame_codename

Name of the version of open sesame

Distribution

Distribution of values for opensesame_codename

Distribution of values for opensesame_codename

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
opensesame_codename Name of the version of open sesame character 0 1 3 0 5 17 0

opensesame_version

Version number of the open sesame used

Distribution

Distribution of values for opensesame_version

Distribution of values for opensesame_version

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
opensesame_version Version number of the open sesame used character 0 1 7 0 5 10 0

Language

Language the participant took the study in

Distribution

Distribution of values for Language

Distribution of values for Language

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
Language Language the participant took the study in character 0 1 18 0 4 20 0

Source

Online (osweb) versus in person (all others) data source

Distribution

Distribution of values for Source

Distribution of values for Source

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
Source Online (osweb) versus in person (all others) data source character 0 1 2 0 4 5 0

Subject

A unique participant identifier, as duplicates and other repeating trials were fixed

Distribution

Distribution of values for Subject

Distribution of values for Subject

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
Subject A unique participant identifier, as duplicates and other repeating trials were fixed character 0 1 4248 0 25 87 0

PPList

The stimulus presentation list for picture picture matching task

Distribution

Distribution of values for PPList

Distribution of values for PPList

95832 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
PPList The stimulus presentation list for picture picture matching task character 95832 0.4998591 8 0 1 12 0

Orientation1

The orientation of the first picture in the picture picture matching task

Distribution

Distribution of values for Orientation1

Distribution of values for Orientation1

95832 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
Orientation1 The orientation of the first picture in the picture picture matching task character 95832 0.4998591 2 0 1 1 0

Orientation2

The orientation of the second picture in the picture picture matching task

Distribution

Distribution of values for Orientation2

Distribution of values for Orientation2

95832 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
Orientation2 The orientation of the second picture in the picture picture matching task character 95832 0.4998591 2 0 1 1 0

Identical

If the two orientations of the pictures matched in the picture picture matching task

Distribution

Distribution of values for Identical

Distribution of values for Identical

95832 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
Identical If the two orientations of the pictures matched in the picture picture matching task character 95832 0.4998591 2 0 1 1 0

Picture1

Name of the first picture in the picture picture matching task

Distribution

Distribution of values for Picture1

Distribution of values for Picture1

95832 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
Picture1 Name of the first picture in the picture picture matching task character 95832 0.4998591 48 0 8 22 0

Picture2

Name of the secon picture in the picture picture matching task

Distribution

Distribution of values for Picture2

Distribution of values for Picture2

95832 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
Picture2 Name of the secon picture in the picture picture matching task character 95832 0.4998591 48 0 8 22 0

Missingness report

Codebook table

JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "Object Orientation across languages",
  "description": "This dataset includes the raw trial data from the PSA002: Object Orientation Across Languages Study. \n\nMental simulation theories of language comprehension propose that people automatically create mental representations of objects mentioned in sentences. Representation is often measured with the sentence-picture verification task, in which participants first read a sentence implying the shape/size/color/object orientation and, on the following screen, a picture of an object. Participants then verify if the pictured object either matched or mismatched the implied visual information mentioned in the sentence. Previous studies indicated the match advantages of shapes, but findings concerning object orientation were mixed across languages. This registered report describes our investigation of the match advantage of object orientation across 18 languages, which was undertaken by multiple laboratories\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name                |label                                                                                                    | n_missing|\n|:-------------------|:--------------------------------------------------------------------------------------------------------|---------:|\n|PSA_ID              |Lab identification code                                                                                  |         0|\n|subject_nr          |Original, lab assigned subject number                                                                    |         0|\n|LAB_SEED            |Original, lab assigned seed number for randomization                                                     |     67464|\n|datetime            |Date and time of the study                                                                               |         0|\n|logfile             |Original location of the saved log                                                                       |     67416|\n|task_order          |                                                                                                         |    129498|\n|List                |List file for the presentation of the stimuli                                                            |     95778|\n|Match               |Match or Mismatch of the sentence and picture for sentence picture trials. F indicates practical trials. |     95778|\n|Orientation         |Direction of the stimuli picture presented on the screen                                                 |     95778|\n|PList               |List file for the practice stimuli presentation                                                          |    101514|\n|Probe               |Sentence seen in the sentence picture matching task                                                      |     95778|\n|Target              |Object seen in the sentence picture matching task                                                        |     95778|\n|response_time       |Response time to determine if objects or sentence/picture matched                                        |         0|\n|correct             |Indicates if the participant answered the trial correctly                                                |         1|\n|opensesame_codename |Name of the version of open sesame                                                                       |         0|\n|opensesame_version  |Version number of the open sesame used                                                                   |         0|\n|Language            |Language the participant took the study in                                                               |         0|\n|Source              |Online (osweb) versus in person (all others) data source                                                 |         0|\n|Subject             |A unique participant identifier, as duplicates and other repeating trials were fixed                     |         0|\n|PPList              |The stimulus presentation list for picture picture matching task                                         |     95832|\n|Orientation1        |The orientation of the first picture in the picture picture matching task                                |     95832|\n|Orientation2        |The orientation of the second picture in the picture picture matching task                               |     95832|\n|Identical           |If the two orientations of the pictures matched in the picture picture matching task                     |     95832|\n|Picture1            |Name of the first picture in the picture picture matching task                                           |     95832|\n|Picture2            |Name of the secon picture in the picture picture matching task                                           |     95832|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "identifier": "https://osf.io/e428p/",
  "creator": "Erin M. Buchanan",
  "citation": "Chen et al. (2023). Investigating Object Orientation Effects Across 18 Languages. Registered Report.",
  "url": "https://osf.io/e428p/",
  "datePublished": "2023-02-10",
  "temporalCoverage": "2019-2021",
  "spatialCoverage": "Online and in Person",
  "keywords": ["PSA_ID", "subject_nr", "LAB_SEED", "datetime", "logfile", "task_order", "List", "Match", "Orientation", "PList", "Probe", "Target", "response_time", "correct", "opensesame_codename", "opensesame_version", "Language", "Source", "Subject", "PPList", "Orientation1", "Orientation2", "Identical", "Picture1", "Picture2"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "PSA_ID",
      "description": "Lab identification code",
      "@type": "propertyValue"
    },
    {
      "name": "subject_nr",
      "description": "Original, lab assigned subject number",
      "@type": "propertyValue"
    },
    {
      "name": "LAB_SEED",
      "description": "Original, lab assigned seed number for randomization",
      "@type": "propertyValue"
    },
    {
      "name": "datetime",
      "description": "Date and time of the study",
      "@type": "propertyValue"
    },
    {
      "name": "logfile",
      "description": "Original location of the saved log",
      "@type": "propertyValue"
    },
    {
      "name": "task_order",
      "description": "",
      "@type": "propertyValue"
    },
    {
      "name": "List",
      "description": "List file for the presentation of the stimuli",
      "@type": "propertyValue"
    },
    {
      "name": "Match",
      "description": "Match or Mismatch of the sentence and picture for sentence picture trials. F indicates practical trials.",
      "@type": "propertyValue"
    },
    {
      "name": "Orientation",
      "description": "Direction of the stimuli picture presented on the screen",
      "@type": "propertyValue"
    },
    {
      "name": "PList",
      "description": "List file for the practice stimuli presentation",
      "@type": "propertyValue"
    },
    {
      "name": "Probe",
      "description": "Sentence seen in the sentence picture matching task",
      "@type": "propertyValue"
    },
    {
      "name": "Target",
      "description": "Object seen in the sentence picture matching task",
      "@type": "propertyValue"
    },
    {
      "name": "response_time",
      "description": "Response time to determine if objects or sentence/picture matched",
      "@type": "propertyValue"
    },
    {
      "name": "correct",
      "description": "Indicates if the participant answered the trial correctly",
      "@type": "propertyValue"
    },
    {
      "name": "opensesame_codename",
      "description": "Name of the version of open sesame",
      "@type": "propertyValue"
    },
    {
      "name": "opensesame_version",
      "description": "Version number of the open sesame used",
      "@type": "propertyValue"
    },
    {
      "name": "Language",
      "description": "Language the participant took the study in",
      "@type": "propertyValue"
    },
    {
      "name": "Source",
      "description": "Online (osweb) versus in person (all others) data source",
      "@type": "propertyValue"
    },
    {
      "name": "Subject",
      "description": "A unique participant identifier, as duplicates and other repeating trials were fixed",
      "@type": "propertyValue"
    },
    {
      "name": "PPList",
      "description": "The stimulus presentation list for picture picture matching task",
      "@type": "propertyValue"
    },
    {
      "name": "Orientation1",
      "description": "The orientation of the first picture in the picture picture matching task",
      "@type": "propertyValue"
    },
    {
      "name": "Orientation2",
      "description": "The orientation of the second picture in the picture picture matching task",
      "@type": "propertyValue"
    },
    {
      "name": "Identical",
      "description": "If the two orientations of the pictures matched in the picture picture matching task",
      "@type": "propertyValue"
    },
    {
      "name": "Picture1",
      "description": "Name of the first picture in the picture picture matching task",
      "@type": "propertyValue"
    },
    {
      "name": "Picture2",
      "description": "Name of the secon picture in the picture picture matching task",
      "@type": "propertyValue"
    }
  ]
}`